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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Lawrence, Linju Shreelekshmi, R. Department of Computer Science and Engineering College of Engineering Trivandrum Affiliated to APJ Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India Department of Computer Applications College of Engineering Trivandrum Affiliated to APJ Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India

ISBN: (纸本)9789819995202

Latest developments in video editing or manipulation tools have facilitated effortless alteration of video content without any discernible traces left behind. As a result, it is imperative to subject video data to an integrity verification process before utilizing it as evidence. This paper presents a novel, lightweight approach for verifying the integrity of video data. The proposed method uses Hash-based Message Authentication Code (HMAC) and Elliptic Curve Diffie-Hellman Key Exchange utilizing Curve 25519 (X25519) with BLAKE2b. Nodes store video verification codes that are generated for video clips of a specific predetermined size. To enhance the security level, each node stores the nested HMAC value of the prior node. The integrity check involves the regeneration and comparison of nested HMAC of the node. The proposed method’s experimental results demonstrate better performance in terms of both speed and security when compared to state-of-the-art methods. With minimal additional storage requirements, our method can identify any type of forgery on any video file, at any given time, by an authorized individual. Security analysis indicates that the method can withstand a range of attacks, such as timing attacks, key substitution attacks, side channel attacks, and brute force attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Side channel attack

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Utilizing the Prophet Model for Time Series Based Stock Price Forecasting 5

Utilizing the Prophet Model for Time Series Based Stock Pric...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Pant, Janmejay Pant, Hitesh Kumar Kotlia, Priyanshi Singh, Devendra Sharma, Vaibhav Kumar Bhatt, Jaishankar Graphic Era Hill University Department of Computer Science and Engineering Bhimtal Campus India Kumaun University Department of Management Studies Uttarakhand Nainital India Amrapali Group of Institutes Department of Computer Applications Uttarakhand Haldwani India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350379945

Time series analysis and forecasting play a crucial role due to their extensive application across various practical domains. Time series data represents a sequential order or collection of data points depicting the values of a variable at regular time intervals. The stock market is widely regarded as one of the most intricate financial systems, comprising numerous components or stocks, whose prices fluctuate significantly over time. Stock market forecasting aims to reveal the trends within the market as they evolve. All investors in the stock market strive to maximize returns on their investments while minimizing associated risks Due to the high sensitivity and rapid changes in stock markets, the primary objective of stock-trend prediction is to devise innovative approaches that can anticipate stocks leading to substantial profits. A statistical model capable of predicting Indian stock prices will be constructed using time series data from the Indian stock market. In our study, we utilize daily stock price data from a prominent company listed on the National Stock Exchange (NSE) of India. © 2024 IEEE.

关键词： Marketplaces

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet 2

Detection of Mango Leaves Disease using CSUBW based Residual...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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Information system for flood monitoring based on IoT and AI 2

Information system for flood monitoring based on IoT and AI

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Wilson, Allan J Pon Bharathi, A. Anoop, M. Angelin Jeba Malar, J. Amrita College of Engineering and Technology Department of Electronics and Communication Engineering Nagercoil India Alpha Arts and Science College Department of Computer Applications Chennai India A.M.Jain College Department of Computer Science Chennai India

ISBN: (纸本)9798350348002

One of the most significant areas where realistic solutions have been put into place to minimise harm is flood management. In this research project, we are creating an IoTenabled adaptive AI technique for effective fluid management. Floods in Kerala, India, have recently seriously damaged the environment, infrastructure, and population. Various IoT devices gathered information on temperature, precipitation rates, and other variables. This study uses a real-time flood visualisation technology to provide an information system for flood monitoring. A system is constructed by first creating two systems: a client system comprised of hardware for detecting floods and a server system comprised of software for monitoring floods and distributing the collected data. All of the information on the flood in Kerala is compiled and split in halves, with the first set serving as training data and the second as test data. The suggested hybrid model is trained using six different machine learning models, and it achieves a maximum accuracy of 99.64%. We were able to prevent losses because to the system's seamless design. Research reveals that the client of the flood detection system may deliver real-time data on flood levels, weather conditions, and temperatures. Additionally, data from client systems can be collected by flood monitoring information systems and stored in MySQL. © 2023 IEEE.

关键词： Visualization

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An Intellectual Diffused Configuration for High-Level Edge Network Elasticity 3

An Intellectual Diffused Configuration for High-Level Edge N...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Karthigeyan, A. Sathish Kumar, M. Ali Sabah, Hawraa Fouad, Laith Abbas Alwan, Adil Fouad, Laith Hindustan Institute of Technology & Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

The Network Distribution System is a rapid evolution of the Internet of Everything (IOE). This system enabled the deployment of new solutions that reduce low-latency edge applications close to cloud servers and resilient edge applications to edge networks close to IoT devices. The network edge must make intelligent decisions that require fast response times and a wide range of edge application requirements. It also requires a heterogeneous dynamic edge network with constrained resources. Increasing the edge network capacity level increases the frequency and latency of many edge devices such as smart cars, robots, and smart IoT devices. With dynamic edge response time, applications automatically adapt to resilience requirements. This paper describes the role of edge networks. It also employs a lightweight, self-adapting, decentralised mechanism for deploying edge applications. It explains optimization methods for edge applications based on IoT technology. Finally, this paper increases the performance of smart waste management system by using the elasticity by reducing the latency of edge network. © 2023 IEEE.

关键词： Elasticity

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Malware Detection using Dynamic Analysis

Malware Detection using Dynamic Analysis

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2023 International Conference on Advances in Intelligent Computing and applications, AICAPS 2023

作者： Anandhi, V. Vinod, P. Menon, Varun G. Abhijith Krishna, E.R. Shilesh, Akshay Viswam, Akshay Shafiq, Amin Scms School of Engineering and Technology Department of Computer Science and Engineering Ernakulam India Affiliated to Apj Abdul Kalam Technological University Kerala Thiruvananthapuram India Cochin University of Science and Technology Department of Computer Applications Kerala India

ISBN: (纸本)9798350333817

Malware detection is an indispensable factor in the security of internet-oriented machines. The number of threats have been increased day by day. Malware analysis is a process of performing analysis and a study of the components and behavior of malware. The use of dynamic analysis will help the system to classify malware more accurately and to detect any malware samples. Dynamic analysis is a method in which the malware runs in a Sandbox environment, and artifacts are collected. The system uses Cuckoo Sandbox for executing the malware samples in a controlled environment. The system compares bidirectional long short-term memory and convolutional neural network models for machine learning algorithms to detect and classify the malware samples. Unlike a typical signature-based detection, where patterns are checked in the source file, a type of static detection, here, dynamic analysis is used to extract necessary reports, which are then preprocessed to get features like dynamic link library (dlls), kernel module names, services used, etc. to try creating a list of text, which can explain the behaviour of the executable file. These are tokenized and embedded to obtain numerical data, which is passed to the models. The accuracy of trained models is compared, which describes the performance of the models on the dataset. Thus providing grounds for testing future models and later building a better detection system based on it. © 2023 IEEE.

关键词： Long short-term memory

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Fractal dimension analysis for improved decision-making in non-stationary multi-arm bandit problems: a machine learning approach

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The European Physical Journal Special Topics 2025年 1-16页

作者： Nandhini, S. Kala Pandian, J. Arun Department of Computer Applications School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

In this research, we propose a novel approach to addressing the exploration–exploitation dilemma in multi-armed bandit (MAB) algorithms using fractal dimensions. The fractal dimension is used in the algorithms to represent the reward distributions of arms which represents the uncertainty of the arm in receiving the reward. The fractal dimension of the reward distribution is implemented in the most popular MAB optimization algorithms, such as Epsilon-Greedy, Upper Confidence Bound (UCB), Exponential-weight algorithm for Exploration and Exploitation (EXP3), and Thompson Sampling in this study. The algorithm prefers to choose arm with the least fractal dimension, as a lower fractal dimension represents less uncertainty of the arm. The performance of the fractal-enhanced MAB optimization algorithms is compared with traditional algorithms in non-stationary environments with various numbers of arms. The proposed approach provides a novel way to quantify and utilize the uncertainty of each arm in MAB problems.

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A coherent approach-based fine-tuning of segment anything model plus watershed algorithm for instance segmentation of mitochondria in electron microscopy images

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IEEE Access 2025年 13卷 98088-98105页

作者： Faska, Zahra Khrissi, Lahbib Mountasser, Imadeddine Haddouch, Khalid Akkad, Nabil El Alshathri, Samah El-Shafai, Walid Sidi Mohamed Ben Abdellah University Laboratory of Engineering Systems and Applications ENSA Fez Morocco Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Computer Science Department Riyadh11586 Saudi Arabia Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt

Increasingly available ultrastructural data from a continuously growing diversity of experimental conditions are driving new opportunities for fruitful neuroscientific hypotheses tested in intracellular compartments such as the nanoscale roles of, e.g., the mitochondria. Reliable morphological statistics are based on achieving highly accurate semantic segmentations of EM images. The state-of-the-art deep CNNs can be somewhat brittle;they tend to provide coarse and high-frequency-oscillatory solutions with discontinuities and false positives even for simple mitochondria segmentation. Historically, the current state-of-the-art in medical image segmentation would involve some variant of the encoder-decoder architecture, such as the U-Net architecture. The SAM does not perform as well, since it has not been explicitly trained for the task and does not demonstrate user-interactive, over one billion annotations mostly for natural images. However, the SAM may be applied to segment anything, including medical image segmentation challenging new datasets. This work is aimed at the difficult task of implementing domain adaptation in mitochondria segmentation within EM images obtained from various tissues and species, using deep learning. We do a systematic study to assess SAM's ability to perform segmentation in medical images, measure its performance on volumetric EM datasets, and show that it is powerful at segmenting instances even under challenging imaging conditions. We provide a fine-tuning SAM which can be naturally trained by SAM at an exemplary scale, benefiting from a diverse and large dataset over one million image masks in 11 modalities. This model would be able to perform precise segmentation for a wide range of targets under various imaging conditions, at the level of performance of specialized U-Net models, or even better. A visual comparison is shown between our fine-tuning SAM model and U-Net, along with an examination of different watershed post-processing st

关键词： Semantic Segmentation

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A Proposed Architecture of Mobile Service Provider Based Interactive Systems 8

A Proposed Architecture of Mobile Service Provider Based Int...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Magoo, Chandni Singh, Manjeet J.C. Bose University of Science and Technology Department of Computer Science and Engineering Faridabad India Manav Rachna University Department of Computer Science and Technology Faridabad India Department of Computer Science and Applications J.C. Bose University of Science and Technology India

ISBN: (纸本)9798350334012

Chatbots are basically designed to imitate any human interaction to automate interaction and support. Numerous benefits of chatbots has led to its increased usage by different organizations to provide essential assistance to its various customers. The current interactive systems are mainly developed from two main concepts namely NLP and Machine Learning. Despite of this, there are certain challenges and limitations of current chatbots. In this paper we will review some of the current chatbots with their limitations and highlight some of the errors identified through survey analysis of different chatbots of some companies like Airtel, Vodafone and Jio etc. A survey is done to analyze the chatbots on various metrics. © 2023 IEEE.

关键词： Machine learning

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Deep Learning Empowered IoT Toothbrush: A Paradigm Shift in Dental Health Monitoring 1

Deep Learning Empowered IoT Toothbrush: A Paradigm Shift in ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Lodha, Nikita Pal, Ankit Das, Sayan Roy, Shreyashi Chakraborty, Sanjay Pandey, Saroj Kumar Techno International New Town Computer Science and Engineering Kolkata India Techno International New Town Aiml Department Kolkata India Gla University Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350330915

The field of dentistry plays a crucial role in maintaining oral health and preventing dental problems. However, many people neglect their dental well-being because of busy schedules or limited access to dental care. Dental issues may go unnoticed until they become more serious and require complex and expensive treatments. To address this issue, we propose an innovative solution that uses dental selfies and advanced machine learning algorithms to identify and classify various dental conditions, such as cavities and gum diseases. These conditions will be categorized as either 'Healthy' or 'Unhealthy.' The main objective of this project is to create a convenient and affordable tool that empowers individuals to detect and diagnose dental problems early on. This tool encourages proactive measures to maintain oral health and seek timely treatment. Our project focuses on developing a technologically advanced toothbrush that incorporates a camera and sensors to classify dental selfies as either indicative of 'Healthy' or 'Unhealthy' oral conditions. To ensure accurate results, we conducted extensive experiments using various Convolutional Neural Network (CNN) models such as VGG16, MobileNet, DenseNet, and VGG19. Through rigorous testing, we achieved a maximum accuracy rate of 88%. This outcome demonstrates the effectiveness of our approach in accurately evaluating oral health conditions based on dental selfies. © 2023 IEEE.

关键词： Convolutional neural network Deep Learning Dentalcare IoT Smart Toothbrush

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